style: reformat the code and imports

docs/chapters/flow/README.md

@@ -206,10 +206,21 @@ input_fn = (b's' for _ in range(10))
 
 If you don't use Python as client, or your client and flow are on different instances. You can hold a flow in running state and use client in other languages to connect to it. Simply:
 
+```python
+import threading
+
+with f:
+    threading.Event().wait()
+```
+
+Please checkout https://github.com/jina-ai/examples/tree/master/helloword-in-cs for a complete example.
+
+**WARNING**: don't use while loop to do the waiting, it is extremely low-efficient:
+
 ```python
 with f:
-    while True:
-        pass
+    while True: # <- dont do that
+        pass # <- dont do that
 ```
 
 ## Use Flow API in YAML

jina/executors/encoders/frameworks.py

@@ -7,7 +7,7 @@
 
 from . import BaseEncoder
 from ..decorators import batching, as_ndarray
-from ..frameworks import BaseOnnxExecutor, BasePaddleExecutor, BaseTorchExecutor, BaseTFExecutor, BaseFrameworkExecutor
+from ..frameworks import BaseOnnxExecutor, BasePaddleExecutor, BaseTorchExecutor, BaseTFExecutor
 from ...helper import is_url
 
 

jina/executors/encoders/image/paddlehub.py

@@ -11,6 +11,7 @@ class ImagePaddlehubEncoder(BaseCVPaddlehubEncoder):
     Internally, :class:`ImagePaddlehubEncoder` wraps the models from `paddlehub`.
     https://github.com/PaddlePaddle/PaddleHub
     """
+
     def __init__(self, *args, **kwargs):
         """
 

jina/executors/encoders/image/tfkeras.py

@@ -3,8 +3,8 @@
 
 import numpy as np
 
-from ...decorators import batching, as_ndarray
 from ..frameworks import BaseCVTFEncoder
+from ...decorators import batching, as_ndarray
 
 
 class KerasImageEncoder(BaseCVTFEncoder):

jina/executors/encoders/nlp/farm.py

@@ -3,8 +3,8 @@
 
 import numpy as np
 
-from ...decorators import batching, as_ndarray
 from ..frameworks import BaseTextTorchEncoder
+from ...decorators import batching, as_ndarray
 
 
 class FarmTextEncoder(BaseTextTorchEncoder):

jina/executors/encoders/nlp/flair.py

@@ -5,8 +5,8 @@
 
 import numpy as np
 
-from ...decorators import batching, as_ndarray
 from ..frameworks import BaseTextTorchEncoder
+from ...decorators import batching, as_ndarray
 
 
 class FlairTextEncoder(BaseTextTorchEncoder):

jina/executors/encoders/nlp/paddlehub.py

@@ -3,8 +3,8 @@
 
 import numpy as np
 
-from ...decorators import batching, as_ndarray
 from ..frameworks import BaseTextPaddlehubEncoder
+from ...decorators import batching, as_ndarray
 
 
 class TextPaddlehubEncoder(BaseTextPaddlehubEncoder):

jina/executors/encoders/nlp/transformer.py

@@ -5,10 +5,10 @@
 
 import numpy as np
 
+from .. import BaseEncoder
+from ..frameworks import BaseTextTFEncoder, BaseTextTorchEncoder
 from ..helper import reduce_mean, reduce_max, reduce_min, reduce_cls
 from ...decorators import batching, as_ndarray
-from ..frameworks import BaseTextTFEncoder, BaseTextTorchEncoder
-from .. import BaseEncoder
 
 
 class BaseTransformerEncoder(BaseEncoder):

jina/executors/encoders/video/paddlehub.py

@@ -1,7 +1,6 @@
 __copyright__ = "Copyright (c) 2020 Jina AI Limited. All rights reserved."
 __license__ = "Apache-2.0"
 
-
 from ..frameworks import BaseCVPaddlehubEncoder
 
 

jina/executors/encoders/video/torchvision.py

@@ -12,6 +12,7 @@ class VideoTorchEncoder(BaseCVTorchEncoder, BaseVideoEncoder):
     Internally, :class:`VideoTorchEncoder` wraps the models from `torchvision.models`.
     https://pytorch.org/docs/stable/torchvision/models.html
     """
+
     def __init__(self, *args, **kwargs):
         """
 

jina/executors/indexers/keyvalue/leveldb.py

@@ -5,7 +5,6 @@
 from typing import Union
 
 from google.protobuf.json_format import Parse
-
 from jina.executors.indexers.keyvalue.proto import BasePbIndexer
 from jina.executors.indexers.keyvalue.proto import jina_pb2
 

jina/executors/indexers/keyvalue/proto.py

@@ -6,7 +6,6 @@
 from typing import Union
 
 from google.protobuf.json_format import Parse
-
 from jina.executors.indexers import BaseKVIndexer
 from jina.proto import jina_pb2
 

jina/executors/indexers/vector/numpy.py

@@ -5,7 +5,6 @@
 from typing import Tuple, Optional
 
 import numpy as np
-
 from jina.executors.indexers import BaseVectorIndexer